Search Results (16,324)

Background: Skeletal muscle is essential not only for structural integrity but also metabolic homeostasis. Muscle atrophy, the loss of muscle mass and function, is closely linked to chronic and metabolic disorders and is driven by chronic inflammation, oxidative stress, impaired myogenesis, and disrupted protein homeostasis. The present study aimed to evaluate the protective effects and underlying mechanisms of Rosa canina extract (RCE), a polyphenol-rich plant known for its antioxidant and anti-inflammatory properties, in vitro and in vivo models of muscle atrophy. Methods: We investigated the effects of RCE in TNF-α-treated L6 myotubes and a mouse model (eight-week-old male C57BL/6N) of immobilization-induced muscle atrophy. Markers of inflammation, oxidative stress, myogenesis, protein turnover, and anabolic signaling were analyzed via RT-PCR, Western blotting and ELISA. Muscle mass, performance, micro-CT imaging, and histological cross-sectional area were assessed in vivo. Results: RCE suppressed pro-inflammatory cytokines, restored antioxidant enzyme expression, and preserved myogenic markers. It inhibited muscle proteolysis by downregulating the genes involved in protein degradation and promoted protein synthesis by via activation of the PI3K/Akt/mTOR pathway. In mice, RCE mitigated muscle mass loss, preserved fiber cross-sectional area, improved strength and endurance, and restored muscle volume. Conclusions: RCE attenuated muscle atrophy by targeting inflammation, oxidative stress, proteolysis, and impaired anabolism. These findings highlight RCE as a promising natural therapeutic for preserving muscle health and metabolic homeostasis. Full article

Metal–organic frameworks (MOFs) are novel porous crystalline materials formed through the self-assembly of metal ions and organic ligands. They have various advantages, including tunable chemical and electronic structures, high porosity, and large specific surface areas. Owing to their unique structural and physicochemical properties, MOFs have been widely applied in the fields of catalysis, supercapacitors, sensors, and drug recognition/delivery. However, the intrinsic poor stability and low electrical conductivity of conventional MOFs severely hinder their practical implementation. Graphdiyne (GDY), a unique carbon allotrope, features a new structure composed of both sp²- and sp-hybridized carbon atoms. Its distinct chemical and electronic configuration endow it with exceptional properties such as natural bandgap, uniform in-plane cavities, and excellent electronic conductivity. Integrating MOFs with GDY can effectively overcome the intrinsic limitations of MOFs and expand their potential applications. As emerging hybrid materials, MOF/GDY composites and their derivatives have attracted increasing attention in recent years. This article reviews recent advances in the synthesis strategies of MOF/GDY composites and their derivatives, along with their performance and applications in catalysis, energy storage, and biological sensors. It also discusses the future opportunities and challenges faced in the development of these promising composite materials, aiming to inspire interest and provide scientific guidance. Full article

Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments. Full article

As a form of living cultural heritage, local residential landscapes manifest the essence of long-term, resilient human–land interactions. The Wuling Corridor, a vital ethnic and cultural passage connecting the Central Plains with Southwest China in Chinese history, serves as a crucial region for the mixed residence and cultural exchange of Tujia, Miao, Dong, Han, and other ethnic groups. Within this region, Ganlan stands as both the most representative vernacular architectural heritage and a residential form that is still extensively used, constituting a continuous and unique residential landscape. The spatial distribution patterns of Ganlan are the physical witness of the history of ethnic groups adapting to the complex topographic and cultural conditions. Current research focuses on the case description of single Ganlan forms, failing to systematically investigate the spatial formation mechanisms of Ganlan as a residential landscape from a geographical continuum perspective. Therefore, this study establishes a geographical database encompassing 9425 Ganlan samples from the Wuling Corridor. It integrates the geographic information system (GIS) with clustering algorithms to systematically identify the distribution patterns of Ganlan within specific geographic–cultural units and their coupling relationships with natural environments. It conducts quantitative analysis on the key driving factors concerning the emergence and evolution of Ganlan in the study area; the findings reveal the following: (1) Ganlan buildings exhibit a spatially aggregated distribution pattern along major water systems, demonstrating characteristics of multi-ethnic sharing and spatial interweaving. (2) Their distribution is constrained by natural geographical factors and influenced by the transmission pathways of construction techniques during ancient ethnic migrations to the southwest China. (3) Within multi-ethnic settlement structures, inter-ethnic cultural interactions (particularly with Central Plains culture) serve as a key driving force for the typological evolution of Ganlan. (4) The evolutionary lineage of “full-Ganlan,” “semi-Ganlan,” and “courtyard-style Ganlan” systematically demonstrates the dynamic adaptive capacity of local residential systems. Additionally, by integrating massive Ganlan heritage data with multiple spatial analysis methods, the study serves as a typical case study illuminating the adaptive strategies and resilience mechanisms of Ganlan as a local residential landscape formed in response to the environmental conditions and social changes. Also, it provides a scientific basis for the holistic conservation of architectural heritages shared by multiple ethnic groups and the integrated development of local cultural tourism industries. Full article

As drones become increasingly integrated into civilian and industrial domains, the demand for natural and accessible control interfaces continues to grow. Conventional manual controllers require technical expertise and impose cognitive overhead, limiting their usability in dynamic and time-critical scenarios. To address these limitations, this paper presents a multilingual voice-driven control framework for quadrotor drones, enabling real-time operation in both English and Arabic. The proposed architecture combines offline Speech-to-Text (STT) processing with large language models (LLMs) to interpret spoken commands and translate them into executable control code. Specifically, Vosk is employed for bilingual STT, while Google Gemini provides semantic disambiguation, contextual inference, and code generation. The system is designed for continuous, low-latency operation within an edge–cloud hybrid configuration, offering an intuitive and robust human–drone interface. While speech recognition and safety validation are processed entirely offline, high-level reasoning and code generation currently rely on cloud-based LLM inference. Experimental evaluation demonstrates an average speech recognition accuracy of 95% and end-to-end command execution latency between 300 and 500 ms, validating the feasibility of reliable, multilingual, voice-based UAV control. This research advances multimodal human–robot interaction by showcasing the integration of offline speech recognition and LLMs for adaptive, safe, and scalable aerial autonomy. Full article

In response to the widespread decline in ecosystem service value (ESV) caused by rapid industrialization and urbanization-driven land-use transitions in Coastal China—characterized by shrinking farmland and expanding built-up land and crystallized in the “core-city sprawl and surrounding-farmland encroachment” pattern—this study integrated land-use and socioeconomic data from 1980 to 2020. Employing the equivalent-factor method and Geodetector model, we quantified the spatiotemporal evolution of ESV and its driving mechanisms across the entire coastal region. The results show that (i) the total ESV experienced a fluctuating increase. (ii) Spatially, the ESV exhibited a “high in the south, low in the north, and higher inland than along the immediate coast” pattern, with mountain–hill belts and estuarine wetlands in the south forming high-value clusters, whereas the Bohai Rim in the north emerged as a low-value zone. (iii) Socioeconomic factors increasingly dominated the driving forces, while NDVI became the most influential natural factor; the interactions between the drivers consistently produced bi-factor enhancement effects. These findings provide a scientific basis for implementing the “Two-Mountains Theory” and optimizing coastal territorial spatial planning. Full article

Base excision repair (BER) is an important mechanism for maintaining genomic integrity and preventing DNA damage and mutations induced by oxidative stress. This study aimed to examine the relationship between oxidative stress and BER activity in newborn piglets by supplementing their mothers’ diets during pregnancy with long-chain n-3 polyunsaturated fatty acids (PUFAs) from algal and fish oils, provided either in natural form or as nanoparticles. BER enzyme activity was assessed using a nicking assay, and their gene expression levels by RT-qPCR in the livers of pregnant gilts and their offspring. Preliminary results indicated that maternal supplementation with oils rich in long-chain n-3 PUFAs significantly reduced (by 32%) BER capacity in the livers of their offspring. A corresponding decrease in mRNA expression of BER genes (TDG, MPG, OGG1) was observed in piglets from gilts receiving fish and algal oil supplements. Maternal supplementation with long-chain n-3 PUFAs may protect foetuses and neonates against oxidative stress, reducing DNA damage and enhancing genomic stability, which could positively influence early postnatal growth. The observed reduction in BER enzyme activity in newborn piglets likely reflected improved DNA integrity, and natural oil forms appeared more effective than their nanoparticle formulations. Disparities in socioeconomic areas related to access to functional foods with health-promoting properties highlight the importance of targeted strategies that integrate local systems and promote nutritional equity. Full article

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R²) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R² of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station. Full article

There is a growing need for comprehensive and transparent frameworks in bibliometric evaluation that support fairer assessments and capture the multifaceted nature of research performance. This study proposes a novel methodology for identifying top-performing researchers based on a composite performance index (CPI). Unlike existing rankings, this framework presents a multidimensional approach by integrating sixteen weighted bibliometrics metrics, spanning research productivity, citation, publications in top journal percentiles, authorship roles, and international collaboration, into a single CPI, enabling a more nuanced and equitable evaluation of researcher performance. Data were retrieved from SciVal for 1996–2025. Two ranking exercises were conducted with Kazakhstan as the analytical unit. Subject-specific rankings identified the top 1% authors within different research areas, while subject-independent rankings highlighted the overall top 1%. CPI distributions varied markedly across disciplines. A comparative analysis with the Stanford/Elsevier global top 2% list was conducted as additional benchmarking. The results highlight that academic excellence depends on a broad spectrum of strengths beyond just productivity, particularly in competitive disciplines. The CPI provides a consistent and adaptable tool for assessing and recognizing research performance; however, future refinements should enhance data coverage, improve representation of early-career researchers, and integrate qualitative aspects. Full article

Bovine colostrum stands out as a natural supplement with rich bioactive components that attract attention for its therapeutic potential in the maintenance and improvement of gastrointestinal (GI) health. The major bioactive components of bovine colostrum include immunoglobulin (Ig) (especially immunoglobulin G), lactoferrin (LF), growth Factors (IGF-I, TGF-β, EGF), oligosaccharides (OS), and bioactive peptides. These components play a role in epithelial repair, suppression of inflammation, balancing the microbiota, and enhancing the mucosal barrier. Various animal models and recent human studies show that bovine colostrum has various positive effects against gastrointestinal tract diseases such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), non-steroidal anti-Inflammatory drug (NSAID)-induced enteropathy, and necrotizing enterocolitis (NEC). These effects include preservation of epithelial integrity, reduction of inflammatory markers, and improvement of intestinal permeability. Studies on the tolerability and efficacy profiles of various bovine colostrum formulations for oral, oropharyngeal, and enteral administration are increasing. In this review, the multifaceted effects of bovine colostrum on the gastrointestinal tract are explained at a mechanistic level, and potential areas of study for clinical translation are presented. Bovine Colostrum stands out as a promising natural biotherapeutic agent for both preventive and therapeutic approaches. Full article

Storm surges are the leading marine disaster in China’s coastal cities, with their impacts exacerbated by climate change and rapid urbanization. Despite their significance, most existing studies focus on a single scale, neglecting the complex, multi-scale nature of urban resilience and the interrelated governance strategies needed to address storm surge risks. This study introduces a dual-scale resilience indicator system—macro (prefecture-level cities) and micro (coastal buffer grids)—within the “exposure–sensitivity–adaptation” framework, utilizing multi-source data for a comprehensive assessment. This research also explores the impact mechanisms of storm surges on urban areas and proposes zonal governance strategies. Findings indicate that resilience varies spatially in Chinese coastal cities, with a pattern of “high resilience in the north, low resilience in the south, and a mix in the center.” At the macro scale, key limitations include policy implementation, infrastructure capacity, and social vulnerability. At the micro scale, factors such as inadequate green space, increased impervious surfaces, limited shelter access, and low utility network density lead to the emergence of “low-resilience units” in ecologically sensitive and mixed coastal zones. The study further reveals the synergies between resilience drivers across scales, emphasizing the need for integrated cross-scale governance. This research advances resilience theory by expanding spatial scales and refining indicator systems, while proposing a zonal governance framework tailored to resilience gradation. It offers a quantitative basis and practical strategies for fostering “safe cities” and advancing “adaptive spatial planning” in the context of sustainable development. Full article

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

This study examines the integration of digital fabrication technologies into the design and production of mycelium-based components, addressing the growing demand for sustainable and innovative interior design solutions. Using a parametric design approach, modular and customized suspended ceiling elements were developed for a specific interior setting to explore a material-specific design approach for mycelium-based components. Three-dimensional printing was employed to produce molds, which were subsequently tested with plaster, silicone, and mycelium across three different scales. Experimental observations focused on the overall form, surface details, growth behavior and dimensional accuracy, systematically capturing volumetric deviations arising from the living nature of the material. In parallel, acoustic performance was evaluated through simulations using the Sabine method. The untreated condition demonstrated the longest reverberation times, whereas conventional panels achieved reductions consistent with typical comfort standards. Prototypes produced with mycelium yielded measurable decreases in reverberation time compared to the untreated condition, particularly within the speech frequency range, and approached the performance of standard acoustic panels. These findings suggest that mycelium-based components, when further optimized in terms of density and geometry, hold the potential to contribute both aesthetic and acoustic value within sustainable interior environments. Full article

Biocatalysis is fundamental to biological processes and sustainable chemical productions. Over time, the biocatalysis strategy has been widely researched. Initially, biomanufacturing and catalysis of high-value chemicals were carried out through direct immobilization and application of biocatalysts, including natural enzymes and living cells. With the evolution of green chemistry and environmental concern, hybrid photoelectro-biocatalysis (HPEB) platforms are seen as a new approach to enhance biocatalysis. This strategy greatly expands the domain of natural biocatalysis, especially for bio-based components. The selective valorization of renewable furan derivatives, such as 5-hydroxymethylfurfural (HMF) and furfural, is central to advancing biomass-based chemical production. Biocatalysis offers high chemo-, regio-, and stereo-selectivity under mild conditions compared with traditional chemical catalysis, yet it is often constrained by the costly and inefficient regeneration of redox cofactors like NAD(P)H. Photoelectrocatalysis provides a sustainable means to supply reducing equivalents using solar or electrical energy. In recent years, hybrid systems that integrate biocatalysis with photoelectrocatalysis have emerged as a promising strategy to overcome this limitation. This review focuses on recent advances in such systems, where photoelectrochemical platforms enable in situ cofactor regeneration to drive enzymatic transformations of furan-based substrates. We critically analyze representative coupling strategies, materials and device configurations, and reaction engineering approaches. Finally, we outline future directions for developing efficient, robust, and industrially viable hybrid catalytic platforms for green biomass valorization. Full article

Collaborative clustering is an ensemble technique that enhances clustering performance by simultaneously and synergistically processing multiple data dimensions or tasks. This is an active research area in artificial intelligence, machine learning, and data mining. A common approach to co-clustering is based on non-negative matrix factorization (NMF). While widely used, NMF-based co-clustering is limited by its bilinear nature and fails to capture the multilinear structure of data. With the objective of enhancing the effectiveness of non-negative Tucker decomposition (NTD) in image clustering tasks, in this paper, we propose a dual-graph constrained sparse non-negative Tucker decomposition NTD (GDSNTD) model for co-clustering. It integrates graph regularization, the Frobenius norm, and an $l_1$ norm constraint to simultaneously optimize the objective function. The GDSNTD mode, featuring graph regularization on both factor matrices, more effectively discovers meaningful latent structures in high-order data. The addition of the $l_1$ regularization constraint on the factor matrices may help identify the most critical original features, and the use of the Frobenius norm may produce a more highly stable and accurate solution to the optimization problem. Then, the convergence of the proposed method is proven, and the detailed derivation is provided. Finally, experimental results on public datasets demonstrate that the proposed model outperforms state-of-the-art methods in image clustering, achieving superior scores in accuracy and Normalized Mutual Information. Full article